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20222026
most citedDeep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

10 citations · 10 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.SE2026

FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair

Ruize Ma, Yilei Jiang, Shilin Zhang +7

Multimodal Automated Program Repair (MAPR) extends traditional program repair by requiring models to jointly reason over source code, textual issue descriptions, and visual artifac…

cs.SE2026

RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair

Zhongqiang Pan, Chuanyi Li, Wenkang Zhong +3

Automated program repair (APR) struggles to scale from isolated functions to full repositories, as it demands a global, task-aware understanding to locate necessary changes. Curren…

cs.SE2024

On Evaluating the Efficiency of Source Code Generated by LLMs

Changan Niu, Ting Zhang, Chuanyi Li +2

Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code gener…

cs.SE2023

FAIR: Flow Type-Aware Pre-Training of Compiler Intermediate Representations

Changan Niu, Chuanyi Li, Vincent Ng +2

While the majority of existing pre-trained models from code learn source code features such as code tokens and abstract syntax trees, there are some other works that focus on learn…

cs.SE202210 cited

Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

Changan Niu, Chuanyi Li, Bin Luo +1

Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has ena…